PYTHON PROGRAMMING & DATA SCIENCE
PYTHON PROGRAMMING & DATA SCIENCE
UNIT 1
Parts of Python Programming Language: Identifiers, Keywords,
Statements and Expressions, Variables, Operators, Precedence and
Associativity, Data Types, Indentation, Comments, Reading Input, Print
Output, Type Conversions, The type() Function and Is Operator, Dynamic
and Strongly Typed Language,
Control Flow Statements: The if Decision Control Flow Statement, The
if…else Decision Control Flow Statement, The if…elif…else Decision
Control Statement, Nested if Statement, The while Loop, The for Loop,
The continue and break Statements.
Lists: Creating Lists, Basic List Operations, Indexing and Slicing in Lists,
Built-In Functions used on Lists, List Methods, The del Statement.
Dictionaries: Creating Dictionary, Accessing and Modifying key: value
Pairs in Dictionaries, Built-In Functions Used on Dictionaries, Dictionary
Methods, The del Statement.
Tuples and Sets: Creating Tuples, Basic Tuple Operations, Indexing and
Slicing in Tuples, Built-In Functions Used on Tuples
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History:
Python was developed by Guido van Rossum in
the late eighties and early nineties at the National
Research Institute for Mathematics and Computer
Science in the Netherlands .
The name Python was selected from the TV
Show "The Complete Monty Python's Circus",
which was broadcasted in BBC from 1969 to
1974.
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Where we can use Python /Applications:
The most common important application areas are
Desktop Applications
web Applications
databaseApplications
Network Programming
Games
DataAnalysis Applications
Machine Learning
developingArtificial IntelligenceApplications
IOT
1.
2.
3.
4.
5.
6.
7.
8.
9.
Note:
Internally Google and Youtube use Python coding
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Features in Python
There are many features in Python,
some of them are:
1. Easy to code
2. Free and Open Source
3. Object-Oriented Language
4. GUI Programming Support
5. High-Level Language
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Features in Python
6. Extensible feature
7. Python is Portable language
8. Python is Integrated language
9. Interpreted Language
10. Large Standard Library
11. Dynamically Typed Language
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Features in Python
1. Easy to code:
Python is a high-level programming language. Python is
very easy to learn the language as compared to other languages like
C, C#, Javascript, Java, etc.
It is also a developer-friendly language.
2. Free and Open Source:
Python language is freely available at the official website
and can download it from the link www.python.org .
It is open-source, this means that source code is also available to
the public. So we can download it as, use it as well as share it.
3. Object-Oriented Language:
One of the key features of python is Object-Oriented
programming. Python supports object-oriented language and
concepts of classes, objects encapsulation, etc.
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Features in Python
4. GUI Programming Support:
Graphical User interfaces can be made using a
module such as PyQt5, PyQt4, wxPython, or Tkinter python.
PyQt5 is the most popular option for creating graphical apps
with Python.
5. High-Level Language:
Python is a high-level language. When we write programs
in python, we do not need to remember the system architecture, nor
do we need to manage the memory.
6. Extensible feature:
Python is a Extensible language. We can write us some
Python code into C or C++ language and also we can compile that
code in C/C++ language.
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Features in Python
7. Python is Portable language:
Python language is also a portable language. For example,
if we have python code for windows and if we want to run this
code on other platforms such as Linux, Unix, and Mac then we do
not need to change it, we can run this code on any platform.
8. Python is Integrated language:
Python is also an Integrated language because we can easily
integrated python with other languages like c, c++, etc.
9. Interpreted Language:
Python is an Interpreted Language because Python code is
executed line by line at a time.
There is no need to compile python code this makes it easier to
debug our code.
The source code of python is converted into an immediate form
called bytecode.
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Features in Python
10. Large Standard Library:
Python has a large standard library which provides a rich set
of module and functions so no need to write own code for every
single thing.
There are many libraries present in python for such as regular
expressions, unit-testing, web browsers, etc.
11. Dynamically Typed Language:
Python is a dynamically-typed language. That means the
type (for example- int, double, long, etc.) for a variable is decided
at run time not in advance because of this feature we don’t need to
specify the type of variable.
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Python Data Types
Data types are the classification or categorization of
data items.
Data type specifies the type of value.
Python supports the following built-in data types.
1. Scalar Types
2. Sequence Type
3. Mapping Type
4. Set Types
5. Mutable and Immutable Types
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Scalar Types:
Scalar data type contains following data types.
1. int
2. float
3. complex
4. Bool
5. None
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1. int:
In Python, integers are zero, positive or negative whole numbers
without a fractional part and having unlimited precision.
Examples:
-10, 10, 456, 4654654.
Integers can be binary, octal, and hexadecimal values.
Examples:
>>> 0b11011000 # binary
216
>>> 0o12 # octal
10
>>> 0x12 # hexadecimal
15
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The type() method is used to get the class name.
Example:
>>> x=1234567890
>>> type(x)
<class 'int'>
# type of x is int
Note:
The int() function converts a string or float to int.
Example:
>>> int('100')
100
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2. float:
In Python, floating point numbers (float) are positive and
negative real numbers with a fractional part denoted by the decimal
symbol (or) the scientific notation E or e.
Example:
1234.56, 3.142, -1.55, 0.23,
Floats can be separated by the underscore _ .
Example:
123_42.222_013 is a valid float
the float() function to convert string, int to float.
Example:
>>> float('5.5')
5.5
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2. Sequence Type
String:
Astring value is a collection of one or more
characters put in single, double or triple quotes.
List:
A list object is an ordered collection of one or more
data items, not necessarily of the same type, put in square
brackets.
Tuple:
ATuple object is an ordered collection of one or
more data items, not necessarily of the same type, put in
parentheses.
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4.Set Types
set:
Set is mutable, unordered collection of distinct
hashable objects.
Aset object has suitable methods to perform
mathematical set operations like union,
intersection, difference, etc.
frozenset:
Frozenset is immutable version of set whose
elements are added from other iterables.
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5.Mutable and Immutable Types
Numbers, strings, and Tuples are
immutable, which means their contents
can't be altered after creation.
items in a List or Dictionary object can
be modified. It is possible to add, delete,
insert, and rearrange items in a list or
dictionary. Hence, they are mutable
objects.
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Variables:
Variables represents memory locations to store values.
(Or)
Variables are containers for storing data values.
Creating Variables
Python has no command for declaring a variable.
Avariable is created the moment we assign a value .
The equal sign (=) is used to assign values to variables.
Example:
x = 5
y = "cse"
print(x)
print(y)
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Rules for creating variables in Python:
Avariable name must start with a letter or the
underscore character.
Avariable name cannot start with a number.
A variable name can only contain alpha-numeric
characters and underscores (A-z, 0-9, and _ ).
Variable names are case-sensitive (name, Name and
NAME are three different variables).
The reserved words(keywords) cannot be used
naming the variable.
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MultipleAssignments to variables:
Python allows to assign a single value to several variables simultaneously.
Example :
a = b = c = 1
Here, an integer object is created with the value 1, and all three variables are
assigned to the same memory location.
multiple objects can be assigned to multiple variables simultaneously.
Example :
a, b, c = 1, 2.5, ”cse”
integer object is created with the value 1 assigned to the variable a
float object is created with the value 2.5 assigned to the variable b
one string object with the value "cse" is assigned to the variable c.
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Delete a variable:
Python variables can be deleted using the
command del.
syntax :
del "variable name".
Example:
f=11
del f
27
KEYWORDS
The words which are predefined by
the system compiler are called as
keywords.
Keywords are also known as ”Reserved
Words”.
All keywords must in lower case
characters.
28
KEYWORDS
35 keywords are available in Python.
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Identifiers
Aname in Python program is called identifier.
It can be class name or function name or module name or
variable name
Example :
a = 10
Rules to define identifiers in Python:
1. The only allowed characters in Python are
i) alphabet symbols(either lower case or upper case)
ii) digits(0 to 9)
iii) underscore symbol(_)
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Rules to define identifiers in Python:
2.Identifier should not starts with digit
Example:
123total (x)
total123 (√)
3.Identifiers are case sensitive.
Example:
total=10
TOTAL=999
print(total) #10
print(TOTAL) #999
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Rules to define identifiers in Python:
Note:
1. If identifier starts with _ symbol then it indicates that it
is private.
2. If identifier starts with (two under score symbols)
indicating that strongly private identifier.
3. If the identifier starts and ends with two underscore
symbols then the identifier is language defined special
name,which is also known as magic methods.
Example :
add
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Comments
Single-line comments begins with a hash(#) symbol and
is useful in mentioning that the whole line should be
considered as a comment until the end of line.
AMulti line comment is used to specify comment on
many lines.
In python, triple double quote(“ “ “) and single quote
(‘ ‘ ‘)are used for multi-line commenting.
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Comments
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Types of Operators:
Python language supports the following types of operators.
1. Arithmetic Operators
+, -, *, /, %, **, //
2. Comparison (Relational) Operators
= =, ! =, < >, <, >, <=, >=
3. Assignment Operators
=, +=, -=, *=, /=, %=, **=, //=
4. Logical Operators
and, or, not
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Types of Operators:
5. Bitwise Operators
&, |, ^, ~,<<, >>
6. Membership
Operators in, not
in
7. Identity Operators
is, is not
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Arithmetic Operators:
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Note:
/ operator always performs floating point arithmetic.
Hence it will always returns float value.
Floor division (//) can perform both floating point and
integral arithmetic.
If arguments are int type then result is int type.
If atleast one argument is float type then result is float
type.
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Comparison (Relational) Operators
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Assignment Operators
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Logical Operators
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Bitwise Operators
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Membership Operators
Python‟s membership operators test for membership
in a sequence, such as strings, lists, or tuples.
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Identity Operators
Identity operators compare the memory locations of
two objects.
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Python Operators Precedence
The following table lists all operators from highest
precedence to lowest.
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Expression:
An expression is a combination of variables constants
and operators written according to the syntax of Python
language.
Expressions are evaluated using an assignment statement.
Variable = expression
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Expression:
Example:
a=10 b=22 c=34
x=a*b+c
y=a-b*c
z=a+b+c*c-a
print "x=",x
print "y=",y
print "z=",z
Output:
x= 254
y= -738
z= 1178
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InputAnd Output Statements
Input Statements
The following 2 functions are available to read dynamic
input from the keyboard.
1. raw_input()
2.input()
raw_input()
This function always reads the data from the keyboard in
the form of String Format.
Type casting is used to convert the string value to specific data
type.
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InputAnd Output Statements
Example:
val2= raw_input("Enter the number: ")
print(type(val2))
val2 = int(val2)
print(type(val2))
print(val2)
Input:
enter the number :
243
Output:
<class ‘str’>
<class ‘int’>
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InputAnd Output Statements
input()
input() function can be used to read data directly in our
required format. No need to perform type casting.
Example:
x=input("Enter Value”)
type(x)
Input
10
Durga
10.5
True
Output
int
str
float
bool
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InputAnd Output Statements
Output Statements
print( ) is used to output the given data to the standard output device
(Screen).
Example:
print(‘cse branch')
Output
cse branch
print( ) is also used to display the combination of message and variable value.
num1 = 10 #variale num1 with value 10
print('The value of variable num1 is ' + num1)
Output :
The value of variable num1 is 10
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format() function in Python
str.format() is one of the string formatting methods in Python3, which
allows multiple substitutions and value formatting.
This method concatenate elements within a string through positional
formatting.
Using a Single Formatter :
Syntax :
{ } .format(value)
Parameters :
(value) : Can be an integer, floating point numeric constant, string,
characters or even variables.
Returntype : Returns a formatted string with the value passed as
parameter in the placeholder position.
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Example 1:
print("Sammy has {} balloons.".format(5))
Output:
Sammy has 5 balloons.
Example 2:
x = 5;
y = 10
print('The value of x is {} and y is {}'.format(x,y))
Output:
The value of x is 5 and y is 10
INPUT AND OUTPUT
Using Multiple Formatters :
Syntax :
{ } { } .format(value1, value2)
Parameters :
(value1, value2) : Can be integers, floating point numeric constants,
strings, characters and even variables.
the number of values passed as parameters in format() method must be
equal to the number of placeholders created in the string.
INPUT AND OUTPUT
Example 1:
# Multiple placeholders in format() function
my_string = "{} and {} are science portals."
print (my_string.format("GitHub", "codeacademy"))
# different datatypes can be used in formatting
print ("Hi ! My name is {} and I am {} years old“ .format("User", 19))
# The values passed as parameters are replaced in order of their entry
print ("This is {} {} {} {}“ .format("one", "two", "three", "four"))
Output:
GitHub and codeacademy are science portals.
Hi ! My name is User and I am 19 years old
This is one two three four
INPUT AND OUTPUT
Formatters with Positional and Keyword Arguments :
Syntax :
{0} {1}.format(positional_argument, keyword_argument)
Parameters :
Positional_argument can be integers, floating point numeric constants,
strings, characters and even variables.
Keyword_argument is essentially a variable storing some value, which is
passed as parameter.
INPUT AND OUTPUT
Example:
print("Every {} should know the use of {} {} programming and {}"
.format("programmer", "Open", "Source", "Operating Systems"))
Every programmer should know the use of Open Source programming and
Operating Systems
# Use the index numbers of the values to change the order that
# they appear in the string
print("Every {3} should know the use of {2} {1} programming and {0}"
.format("programmer", "Open", "Source", "Operating
Systems"))
Every Operating Systems should know the use of Source Open
programming and programmer
Control Flow Tools
In python there are 2 types of Control Flow Statements
. Those are
or Decision Making
1. Conditional Statements
Statements
2. Looping Statements
Control Flow Tools
Decision Making:
Conditional statements or Decision
statements are used, when we need
to to perform different actions
depending on whether the specified
condition evaluates to true or false.
the general form of a typical decision
making Structure:
Python programming language
assumes any non-zero and non-
null values as TRUE, and if it is
either zero or null, then it is assumed as
FALSE Value.
Control Flow Tools
Different types of Decision Making Statements are:
1. If statement
2. If…else statement
3.Nested if statements
Note:
Python relies on indentation (whitespace at the
beginning of a line) to define scope in the code. Other
programming languages often use curly-brackets for this
purpose.
Control Flow Tools
IF Statement:
The if statement contains a logical expression using
which data is compared and a decision is made
based on the result of the comparison.
Syntax
if expression:
statement(s)
If the boolean expression evaluates to TRUE, then
the block of statement(s) inside the if statement is
executed.
If boolean expression evaluates to FALSE, then the
first set of code after the end of the if statement(s)
is executed.
Control Flow Tools
The Flowchart is as follows:
Control Flow Tools
Example 1:
#!/usr/bin/python
var1 = 100
if var1:
print "1 - Got a true expression
value"
print var1
var2 = 0
if var2:
print "2 - Got a true expression
value"
print var2
print "Good bye!“
Output:
1 - Got a true
expression value
100
Good bye!
Control Flow Tools
Example 2:
#!/usr/bin/python
x = int(raw_input("Please enter an
integer: "))
if x < 0:
print(“Enter only +ve number")
print("ok")
Result:
Input:
Please enter an integer: 33
Output:
Ok
Input:
Please enter an
integer: -200
Output:
Enter only +ve number
Ok
Control Flow Tools
IF…ELSE Statements
An else statement can be combined with
an if statement.
An else statement contains the block of code that
executes if the conditional expression in the if
statement resolves to 0 or a FALSE value.
Syntax
if expression:
statement(s)
else:
statement(s)
Control Flow Tools
The Flowchart is as follows:
Control Flow Tools
Example 1:
#!/usr/bin/python
var1 = 100
if var1:
print "1 - Got a true expression
value"
print var1
else:
print "1 - Got a false expression
value"
print var1
Output:
1 - Got a true
expression value
100
Control Flow Tools
IF...ELIF...ELSE Statements
The elif statement allows you to check multiple
expressions for TRUE and execute a block of code as
soon as one of the conditions evaluates to TRUE
Syntax
if expression1:
statement(s)
elif expression2:
statement(s)
elif expression3:
statement(s)
else:
statement(s)
Control Flow Tools
The flowchart is as follows:
Control Flow Tools
Example 1:
#!/usr/bin/python
var = 100
if var == 200:
print "1 - Got a true expression value"
print var
elif var == 150:
print "2 - Got a true expression value"
print var
elif var == 100:
print "3 - Got a true expression value"
print var
else:
print "4 - Got a false expression value"
print var
print "Good bye!"
Output:
3 - Got a true
expression value
100
Good bye!
Control Flow Tools
nested IF statements:
There may be a situation when we want to check for
another condition after a condition resolves to true.
In such a situation, we can use the
nested if statements.
In a nested if statement, we can have
an if...elif...else construct inside
another if...elif...else construct.
Control Flow Tools
Syntax is as follows
if expression1:
statement(s)
if expression2:
statement(s)
elif expression3:
statement(s)
elif expression4:
statement(s)
else:
statement(s)
else:
statement(s)
Control Flow Tools
Example 1:
#!/usr/bin/python
var = 100
if var < 200:
print "Expression value is less than 200"
if var == 150:
print "Which is 150"
elif var == 100:
print "Which is 100"
elif var == 50:
print "Which is 50"
elif var < 50:
print "Expression value is less than 50"
else:
print "Could not find true expression"
print "Good bye!"
Output:
Expression value is less than
200
Which is 100
Good bye!
Conditional
execution
execution
conditionals
Nested
conditional
s
• Conditions
change the
flow of
program
execution
•
if
statement
is used in
conditions
Syntax:
if condition:
statement 1
…
• Two
possibilities
(else is used)
Syntax:
if condition:
statement 1
…
else:
statement 2
…
• More than
two
possibilities.
elif
• is used.
Syntax:
if condition:
statement 1
elif condition:
statement 2
elif condition:
statement 3
else:
statement 4
• Another
Condition in
one condition
Syntax:
if condition1:
if
condition2:
statement
1
else:
statement
2
else:
statement 3
Condition
Alternative
als
Chained
Python - Loops
Loop statement allows executing a statement or
group of statement multiple times.
Python programming language provides following
3 types of loops.
1. While loop
2. For loop
3. Nested loops
Python - Loops
While Loop
Syntax:
while expression:
statement(s)
When a given
condition is true then
a statement or group
of statement repeats
which are in the loop
body.
Flow Chart
Python - Loops
Example
#!/usr/bin/python
count = 0
while (count < 5):
print 'The count is:', count
count = count + 1
print "Good bye!“
Output
The count is: 0
The count is: 1
The count is: 2
The count is: 3
The count is: 4
Good bye!
Python - Loops
Using else Statement with While Loop
If the else statement is used with a while loop,
the else statement is executed when the condition
becomes false.
Example
#!/usr/bin/python
count = 10
while count < 5:
print count, " is less than 5"
count = count + 1
else:
print count, " is not less than 5“
Output:
Python - Loops
For Loop
Syntax :-
for iterating_var in sequence:
statements(s)
If a sequence contains an
expression list, it is evaluated
first.
Then, the first item in the
sequence is assigned to the
iterating variable iterating_var.
Next, the statements block is
executed.
Each item in the list is assigned
to iterating_var, and the
Flow Chart
Python - Loops
Example 2:
fruits = ['banana', 'apple',
'mango']
for fruit in fruits:
print 'Current fruit :',
fruit
Output:
Current fruit : banana
Current fruit : apple
Current fruit : mango
Example 1:
s1="ece"
for letter in s1:
print 'Current Letter :',
letter
Output:
Current Letter : e
Current Letter : c
Current Letter : e
Python Nested Loops
Writing one loop inside another loop is called
nested loops.
The syntax for a nested for loop statement is as
follows −
for iterating_var in sequence:
for iterating_var in sequence:
statements(s)
statements(s)
The syntax for a nested while loop statement is as
follows
while expression:
while expression:
statement(s)
statement(s)
Python Nested Loops
Example:
program to find the prime numbers from 2 to 50
#!/usr/bin/python
i = 2
while(i < 25):
j = 2
while(j <= (i/j)):
if not(i%j): break
j = j + 1
if (j > i/j) : print i, " is prime"
i = i + 1
Output:
2 is prime
3 is prime
5 is prime
7 is prime
11 is prime
13 is prime
17 is prime
19 is prime
23 is prime
Control Flow Tools
break and continue Statements, and else Clauses on
Loops:
The break statement is used in loop control
statements such as while, and for statements to
terminate the execution of the loop.
When the keyword break is encountered inside
any loop, control automatically skip entire loop
and passes to the statements available after the
loop.
Syntax: break;
The continue statement is used in while and for
statements to terminate the current iteration of
the loop.
Control Flow Tools
break and continue Statements, and else Clauses on
Loops:
When the keyword continue is encountered inside
any loop, compiler skips the remaining
statements available after the continue statement
and control reaches to next iteration of the loop.
Syntax: continue;
Loop statements may have an else clause; it is executed
when the loop terminates through exhaustion of the list
(with for) or when the condition becomes false (with
while), but not when the loop is terminated by a break
statement.
Python - Loops
Example 2:
#!/usr/bin/python
for letter in 'Python':
if letter == 'h':
continue
print 'Current Letter
:', letter
Output:
Current Letter : P
Current Letter : y
Current Letter : t
Current Letter : o
Current Letter : n
Example 1:
#!/usr/bin/python
for letter in 'Python':
if letter == 'h':
break
print 'Current Letter
:', letter
Output:
Current Letter : P
Current Letter : y
Current Letter : t
Control Flow Tools
range() funtion :
range() function is used to iterate over a sequence
of numbers.
It generates lists containing arithmetic progressions.
Example 1:
>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Example 2:
>>>range(5, 10)
[5, 6, 7, 8, 9]
Control Flow Tools
To iterate over the indices of a sequence, you can
combine range() and len() as follows:
>>>a = [’Mary’, ’had’, ’a’, ’little’, ’lamb’]
>>>for i in range(len(a)):
... print i, a[i]
Output:
1 Mary
2 had
3 a
4 little
5 lamb
Control Flow Tools
Example:
for n in range(2, 10):
for x in range(2, n):
if n % x == 0:
print n, 'equals',
x, '*', n/x
break
else:
print n, 'is a prime
number'
Output:
2 is a prime number
3 is a prime number
4 equals 2 * 2
5 is a prime number
6 equals 2 * 3
7 is a prime number
8 equals 2 * 4
9 equals 3 * 3
Control Flow Tools
pass Statement is used when a statement is
required syntactically but we do not want any
command or code to execute.
The pass statement is a null operation; nothing
happens when it executes.
Python - Loops
Output:
Current Letter : P
Current Letter : y
Current Letter : t
This is pass block
Current Letter : h
Current Letter : o
Current Letter : n
Example :
#!/usr/bin/python
for letter in 'Python':
if letter == 'h':
pass
print 'This is pass
block'
print 'Current Letter :',
letter
Control Flow Tools
pass Statement is used when a statement is
required syntactically but we do not want any
command or code to execute.
The pass statement is a null operation; nothing
happens when it executes.
Built-in Data Types
Built-in Data Types:
Python has the following 4 built-in data types:
:1. Text Type
2. Numeric Types
3. complexSequence Types
4. Mapping Type
str
: int, float
list, tuple, range
: dict
:
complexSequence Types and Mapping Type are called as Data Structures in
Python.
DATA STRUCTURES
List Data Type:
Lists are used to store multiple items in a single variable.
Lists are created using square brackets:
Example:
Create a List:
thislist = ["apple", "banana", "cherry"]
print(thislist)
Output:
['apple', 'banana', 'cherry']
List items are ordered, changeable, and allow duplicate values.
DATA STRUCTURES
List Data Type:
List items are indexed, the first item has index [0], the second item has
index [1]… etc.
Access List Items:
List items are accessed by referring to the index number:
Example 1:
#!/usr/bin/python
thislist = ["apple", "banana", "cherry"]
print(thislist[1])
Output:
banana
DATA STRUCTURES
List Data Type:
Example 2:
#!/usr/bin/python
list1 = ['physics', 'chemistry', 1997, 2000]
list2 = [10, 20, 30, 40, 50, 60, 70 ]
print ("list1[0]: ", list1[0])
Print( "list2[1]: ", list2[1])
Print(list2[0:4])
Output:
list1[0]: physics
list2[1]: 20
DATA STRUCTURES
List Data Type:
Negative indexing means start from the end
-1 refers to the last item, -2 refers to the second last item etc.
[start:end:step]
Example :
#!/usr/bin/python
thislist = ["apple", "banana", "cherry"]
print(thislist[-1])
Output:
cherry
DATA STRUCTURES
List Data Type:
We can specify a range of indexes by specifying where to start and
where to end the range.
When specifying a range, the return value will be a new list with the
specified items.
ending index The search begins from satrting index (included) to
(not included).
Example :
#!/usr/bin/python
thislist = ["apple", "banana", "cherry", "orange", "kiwi", "melon", "mango"]
print(thislist[2:5])
Output:
['cherry', 'orange', 'kiwi']
DATA STRUCTURES
List operations
DATA STRUCTURES
Updating Lists
We can update single or multiple elements of lists by giving the slice on
the left-hand side of the assignment operator, and can add to elements in
to list.
Example :
list = ['physics', 'chemistry', 1997, 2000];
print "Value available at index 2 : “
print list[2]
list[2] = 2001;
print "New value available at index 2 : “
print list[2]
Output:
Value available at index 2 : 1997
New value available at index 2 : 2001
DATA STRUCTURES
Delete List Elements
To remove a list element, we can use either the del statement or the remove()
method.
del statement is used if we know exactly which element(s) to delete.
otherwise use remove() method.
Example :
list1 = ['physics', 'chemistry', 1997, 2000];
print list1
del list1[2];
print "After deleting value at index 2 : “
print list1
Output:
['physics', 'chemistry', 1997, 2000]
After deleting value at index 2 : ['physics', 'chemistry', 2000]
DATA STRUCTURES
The del statement:
For example:
a = [-1, 1, 66.25, 333, 333, 1234.5]
del a[0]
a
[1, 66.25, 333, 333, 1234.5]
del a[2:4]
a
[1, 66.25, 1234.5]
del can also be used to delete entire variables:
del a
DATA STRUCTURES
List Methods
The list data type has some more methods. Some of them are:
1. append(x)
Add an item to the end of the list;
2. extend(L)
Extend the list by appending all the items in the given list;
3. insert(i, x)
Insert an item at a given position. The first argument is the index of the
element before which to insert, so a.insert(0, x) inserts at the front of the
list, and a.insert(len(a), x) is equivalent to a.append(x).
DATA STRUCTURES
List Methods
4. remove(x)
Remove the first item from the list whose value is x. It is an error if there is
no such item.
5. pop([i])
Remove the item at the given position in the list, and return it. If no index
is specified, a.pop() removes and returns the last item in the list.
6. index(x)
Return the index in the list of the first item whose value is x. It is an error if
there is no such item.
DATA STRUCTURES
List Methods
7. count(x)
Return the number of times x appears in the list.
8. sort()
Sort the items of the list, in place.
9. reverse()
Reverse the elements of the list, in place.
DATA STRUCTURES
An example that uses most of the list methods:
a = [66.25, 333, 333, 1, 1234.5]
print a.count(333)
print a.count(66.25)
print a.count(’x’)
Output:
2 1 0
a.insert(2, -1)
a.append(333)
a
Output:
[66.25, 333, -1, 333, 1, 1234.5, 333]
DATA STRUCTURES
[66.25, 333, -1, 333, 1, 1234.5, 333]
a.index(333)
1
a.remove(333)
a
[66.25, -1, 333, 1, 1234.5, 333]
a.reverse()
a
[333, 1234.5, 1, 333, -1, 66.25]
a.sort()
a
[-1, 1, 66.25, 333, 333, 1234.5]
DATA STRUCTURES
Using Lists as Stacks:
The list methods make it very easy to use a
list as a stack, where the last element added is
the first element retrieved (“last-in, first-out”).
To add an item to the top of the stack, use
append().
To retrieve an item from the top of the stack,
use pop() without an explicit index.
DATA STRUCTURES
Using Lists as Stacks:
For example:
stack = [3, 4, 5]
stack.append(6)
stack.append(7)
Print(stack)
[3, 4, 5, 6, 7]
stack.pop()
7
DATA STRUCTURES
Using Lists as Stacks:
stack
[3, 4, 5, 6]
stack.pop()
6
stack.pop()
5
stack
[3, 4]
DATA STRUCTURES
Using Lists as Queue:
It is also possible to use a list as a queue,
where the first element added is the first
element retrieved (“first-in, first-out”).
While appends and pops from the end of list
is fast, doing inserts or pops from the beginning
of a list is slow.
To implement a queue, use collections. deque
which was designed to have fast appends and
pops from both ends.
DATA STRUCTURES
Using Lists as Queue:
Example:
from collections import deque
queue = deque(["Linux", "Programming", "Script"])
print("Original Queue values are:")
print(queue)
queue.append("Ece")
queue.append("Section")
print("After adding Queue values are:")
print(queue)
queue.popleft()
queue.popleft()
print("After Removing Queue values are:")
print(queue)
Output:
Original Queue values are:
deque(['Linux', 'Programming',
'Script'])
After adding Queue values are:
deque(['Linux', 'Programming',
'Script', 'Ece', 'Section'])
After Removing Queue values
are:
deque(['Script', 'Ece', 'Section'])
DATA STRUCTURES
Functional (List) Programming Tools
There are three built-in functions that are very useful .
1. filter() 2. map() 3. reduce().
1. Filter(function, sequence)
Returns a sequence consisting of the items from the sequence for
which function(item) is true.
Example:
def f(x): return x % 2 != 0 and x % 3 != 0
print(filter(f, range(2, 25)))
Output:
[5, 7, 11, 13, 17, 19, 23]
Computes primes up to 25
DATA STRUCTURES
Functional (List) Programming Tools
2. Map(function, sequence)
Calls function(item) for each of the sequence’s items
Example:
def cube(x): return x*x*x
print(map(cube, range(1, 11)))
Output:
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]
Computes the cube for the range of 1 to 11
DATA STRUCTURES
Functional (List) Programming Tools
Reduce(function, sequence)
Returns a single value constructed by calling the binary function
(function)
Example:
def add(x,y): return x+y
print(reduce(add, range(1, 11)))
Output:
55
Computes the sum of the numbers 1 to 10
DATA STRUCTURES
Loop Lists
we can loop through the list items by using a for loop:
For example:
thislist = ["apple", "banana", "cherry"]
for x in thislist:
print(x)
Output:
apple
banana
Cherry
DATA STRUCTURES
we can also loop through the list items by referring to their index number.
Use the range() and len() functions to create a suitable iterable.
For example:
thislist = ["apple", "banana", "cherry"]
for i in range(len(thislist)):
print(thislist[i])
Output:
apple
banana
Cherry
DATA STRUCTURES
we can loop through the list items by using a while loop.
For example:
thislist = ["apple", "banana", "cherry"]
i = 0
while i < len(thislist):
print(thislist[i])
i = i + 1
Output:
apple
banana
Cherry
DATA STRUCTURES
List Comprehensions:
List comprehensions provide a concise way to create lists without resorting to
use of map(), filter() and/or lambda.
List comprehension offers a shorter syntax when you want to create a new list
based on the values of an existing list.
The Syntax
newlist = [expression for item in iterable if condition == True]
The return value is a new list, leaving the old list unchanged.
DATA STRUCTURES
Without list comprehension:
Example:
fruits = ["apple", "banana",
"cherry", "kiwi", "mango"]
newlist = []
for x in fruits:
if "a" in x:
newlist.append(x)
print(newlist)
Output:
['apple', 'banana', 'mango']
With list comprehension:
Example:
fruits = ["apple", "banana", "cherry",
"kiwi", "mango"]
newlist = [x for x in fruits if "a" in x]
print(newlist)
Output:
['apple', 'banana', 'mango']
DATA STRUCTURES
Tuple:
Tuples are used to store multiple items in a single variable.
A tuple is a collection which is ordered and unchangeable.
Tuples are written with round brackets.
Example
Create a Tuple:
thistuple = ("apple", "banana", "cherry")
print(thistuple)
Output:
('apple', 'banana', 'cherry')
Tuple items are orde
D
re
A
d,
T
un
A
cha
S
n
T
ge
R
ab
U
le,
C
an
T
d
U
allo
R
w
E
d
S
uplicate
values.
Tuple items are indexed, the first item has index [0], the
second item has index [1] etc.
Example
my_tuple = ('p','e','r','m','i','t')
print(my_tuple[0])
print(my_tuple[5])
n_tuple = ("mouse", [8, 4, 6], (1, 2, 3))
print(n_tuple[0][3])
print(n_tuple[1][1])
print(my_tuple[-1])
print(my_tuple[-6])
Output:
P
t
s
4
t
p
• Accessing Values in Tuples
• To access values in tuple, use the square brackets
for slicing along with the index or indices to
obtain value available at that index. For example
−
• Live Demo#!/usr/bin/python
• tup1 = ('physics', 'chemistry', 1997, 2000)
• tup2 = (1, 2, 3, 4, 5, 6, 7 )
• print ("tup1[0]: ", tup1[0])
• print ("tup2[1:5]: ", tup2[1:5])
Built-in functions in tuples
Sr.No.
Function with Description
1
cmp(tuple1,
tuple2)Compares elements of both tuples.
2
len(tuple)
Gives the total length of the tuple.
3
max(tuple)
Returns item from the tuple with max value.
4
min(tuple)
Returns item from the tuple with min value.
5
tuple(seq)
Converts a list into tuple.
t1 = tuple()
print('t1=', t1)
# creating a tuple from a list
t2 = tuple([1, 6, 9])
print('t2=', t2)
# creating a tuple from a string
t1 = tuple('Java')
print('t1=',t1)
# creating a tuple from a dictionary
t1 = tuple({4: 'four', 5: 'five'})
print('t1=',t1)
Tuple methods
'for', 'java', 'python')
• Count() Method
• The count() method of Tuple returns the number of times the given
element appears in the tuple.
• Syntax: tuple.count(element)
• # Creating tuples
• Tuple1 = (0, 1, 2, 3, 2, 3, 1, 3, 2)
• Tuple2 = ('python', 'geek', 'python',
• # count the appearance of 3
• res = Tuple1.count(3)
• print('Count of 3 in Tuple1 is:', res)
• # count the appearance of python
• res = Tuple2.count('python')
• print('Count of Python in Tuple2 is:', res)
Example 2: Counting tuples and lists as elements in Tuples
• # Creating tuples
• Tuple = (0, 1, (2, 3), (2, 3), 1, [3, 2], 'geeks', (0,))
• # count the appearance of (2, 3)
• res = Tuple.count((2, 3))
• print('Count of (2, 3) in Tuple is:', res)
• # count the appearance of [3, 2]
• res = Tuple.count([3, 2])
• print('Count of [3, 2] in Tuple is:', res)
• Index() Method
• The Index() method returns the first occurrence of the given element from the
tuple.
• Syntax:
tuple.index(element, start, end)
# Creating tuples
Tuple = (0, 1, 2, 3, 2, 3, 1, 3, 2)
# getting the index of 3
res = Tuple.index(3)
print('First occurrence of 3 is', res)
# getting the index of 3 after 4th
# index
res = Tuple.index(3, 4)
print('First occurrence of 3 after 4th index is:', res)
DATA STRUCTURES
Dictionary:
Dictionaries are used to store data values in key:value pairs.
A dictionary is a collection which is unordered, changeable and does
not allow duplicates.
Dictionaries are written with curly brackets, and have keys and
values:
Example
Create a Dictionary:
thisdict = {
"Rno": 501,
"Name": “KUMAR ",
"Group": "CSE"
}
print(thisdict)
Output:
{'Roll no': 501, 'Name':
‘KUMAR', 'Group': 'CSE'}
DATA STRUCTURES
Dictionary items are unordered, changeable, and does not allow
duplicates.
Dictionary items are presented in key:value pairs, and can be
referred to by using the key name.
Example
my_dict = {'name': 'Jack', 'age': 26}
print(my_dict['name'])
print(my_dict.get('age'))
Output:
Jack
26
Employee = {"Name": "John", "Age": 29, "
salary":25000,"Company":"GOOGLE"}
print(type(Employee))
print("printing Employee data .... ")
print(Employee)
# Creating an empty Dictionary
Dict = {}
print("Empty Dictionary: ")
print(Dict)
# Creating a Dictionary
# with dict() method
Dict = dict({1: ‘keyboard', 2: ‘cpu', 3:‘monitor})
print("\nCreate Dictionary by using dict(): ")
print(Dict)
# Creating a Dictionary
# with each item as a Pair
Dict = dict([(1, 'Devansh'), (2, 'Sharma')])
print("\nDictionary with each item as a pair: ")
print(Dict)
Employee = {"Name": "John", "Age": 29, "salary":25000
,"Company":"GOOGLE"}
print(type(Employee))
print("printing Employee data .... ")
print("Name : %s" %Employee["Name"])
print("Age : %d" %Employee["Age"])
print("Salary : %d" %Employee["salary"])
print("Company : %s" %Employee["Company"])
Python allows dictionary comprehensions.
dictionaries using simple expressions.
We can create
A dictionary
comprehension takes the form {key: value for (key, value) in
iterable}
Python Dictionary Comprehension Example
Here we have two lists named keys and value and we are
iterating over them with the help of zip() function
# Lists to represent keys and values
keys = ['a','b','c','d','e']
values = [1,2,3,4,5]
# but this line shows dict comprehension here
myDict = { k:v for (k,v) in zip(keys, values)}
# We can use below too
# myDict = dict(zip(keys, values))
print (myDict)
name = [ "Manjeet", "Nikhil", "Shambhavi", "Astha" ]
roll_no = [ 4, 1, 3, 2 ]
# using zip() to map values
mapped = dict(zip(name, roll_no))
print(mapped)
name = ["Manjeet", "Nikhil", "Shambhavi", "Astha"]
roll_no = [4, 1, 3, 2]
marks = [40, 50, 60, 70]
# using zip() to map values
mapped = zip(name, roll_no, marks)
# converting values to print as list
mapped = list(mapped)
# printing resultant values
print("The zipped result is : ", end="")
print(mapped)
Iterating Dictionary:
A dictionary can be iterated using for loop as given below.
Example 1
# for loop to print all the keys of a dictionary
Employee = {"Name": "John", "Age": 29, "salary":25000,"Company":"GOOGLE"}
for x in Employee:
print(x)
Output: name, age, salary, company
Name Age salary Company
Name Age salary Company
Example 2
#for loop to print all the values of the dictionary
Employee = {"Name": "John", "Age": 29, "salary":25000,"Company":"GOOGLE"}
for x in Employee:
print(Employee[x])
Output: john, 29, 25000, Google
Example - 3
#for loop to print the values of the dictionary by using values() method.
Employee = {"Name": "John", "Age": 29, "salary":25000,"Company":"GOOGLE"}
for x in Employee.values():
print(x)
Output:john, 29, 25000, Google
John 29 25000 GOOGLE
Example 4
#for loop to print the items of the dictionary by using items() method.
Employee = {"Name": "John", "Age": 29, "salary":25000,"Company":"GOOGLE"}
for x in Employee.items():
print(x)
Python Set
• A Python set is the collection of the unordered items.
Each element in the set must be unique, immutable,
and the sets remove the duplicate elements. Sets are
mutable which means we can modify it after its
creation.
Creating a set
The set can be created by enclosing the comma-
separated immutable items with the curly braces {}.
Python also provides the set() method, which can be
used to create the set by the passed sequence.
Example 1: Using curly braces
Days = {"Monday", "Tuesday", "Wednesday", "Thursda
y", "Friday", "Saturday", "Sunday"}
print(Days)
print(type(Days))
print("looping through the set elements ... ")
for i in Days:
print(i)
Example 2: Using set() method
Days = set(["Monday", "Tuesday", "Wednesday", "Thurs
day", "Friday", "Saturday", "Sunday"])
print(Days)
print(type(Days))
print("looping through the set elements ... ")
for i in Days:
print(i)
# Empty curly braces will create dictionary
set3 = {}
print(type(set3))
# Empty set using set() function
set4 = set()
print(type(set4))
Adding items to the set
• Python provides the add() method and update() method which can be used to add some particular
item to the set. The add() method is used to add a single element whereas the update() method is
used to add multiple elements to the set. Consider the following example.
• Example: 1 - Using add() method
Months = set(["January","February", "March", "April", "May", "June"])
print("\nprinting the original set ... ")
print(months)
print("\nAdding other months to the set...");
Months.add("July");
Months.add ("August");
print("\nPrinting the modified set...");
print(Months)
print("\nlooping through the set elements ... ")
for i in Months:
print(i)
Example - 2 Using update() function
Months = set(["January","February", "March", "April", "May", "
June"])
print("\nprinting the original set ... ")
print(Months)
print("\nupdating the original set ... ")
months.update(["July","August","September","October"]);
print("\nprinting the modified set ... ")
print(Months);
Removing items from the set
• Python provides the discard() method
and remove() method which can be used to
remove the items from the set.
• The difference between these function, using
discard() function if the item does not exist in
the set then the set remain unchanged
whereas remove() method will through an
error.
Example-1 Using discard() method
months = set(["January","February", "March", "April", "May", "June"])
print("\nprinting the original set ... ")
print(months)
print("\nRemoving some months from the set...");
months.discard("January");
months.discard("May");
print("\nPrinting the modified set...");
print(months)
print("\nlooping through the set elements ... ")
for i in months:
print(i)
Output:
• printing the original set ...
• {'February', 'January', 'March', 'April', 'June',
'May'}
• Removing some months from the set...
Printing the modified set... {'February',
'March', 'April', 'June'}
• looping through the set elements ...
• February March April June
Example-2 Using remove() function
months = set(["January","February", "March", "Apri
l", "May", "June"]) print("\nprinting the original se
t ... ")
print(months)
print("\nRemoving some months from the set...");
months.remove("January");
months.remove("May");
print("\nPrinting the modified set...");
print(months)
Pop() method, Python provides the clear() method to remove all the items from the set.
• We can also use the pop() method to remove the item. Generally, the pop()
method will always remove the last item but the set is unordered, we can't
determine which element will be popped from set.
• Consider the following example to remove the item from the set using pop()
method.
Months = set(["January","February", "March", "April", "May", "June"])
print("\nprinting the original set ... ")
print(Months)
print("\nRemoving some months from the set...");
Months.pop();
Months.pop();
print("\nPrinting the modified set...");
print(Months)
• Difference between discard() and remove()
• Despite the fact that discard() and remove() method
both perform the same task, There is one main
difference between discard() and remove().
• If the key to be deleted from the set using discard()
doesn't exist in the set, the Python will not give the
error. The program maintains its control flow.
• On the other hand, if the item to be deleted from the
set using remove() doesn't exist in the set, the Python
will raise an error.
Python Set Operations
• set can be performed mathematical operation such
as union, intersection, difference, and symmetric
difference. Python provides the facility to carry out
these operations with operators or methods. We
describe these operations as follows.
• Union of two Sets
• The union of two sets is calculated by using the pipe
(|) operator. The union of the two sets contains all
the items that are present in both the sets.
• Example 1: using union | operator
Days1 = {"Monday","Tuesday","Wednesday","Th
ursday", "Sunday"}
Days2 = {"Friday","Saturday","Sunday"}
print(Days1|Days2) #printing the uni
on of the sets
Union() method
• Python also provides the union() method which
can also be used to calculate the union of two
sets. Consider the following example.
• Example 2: using union() method
Days1 = {"Monday","Tuesday","Wednesday","Thurs
day"}
Days2 = {"Friday","Saturday","Sunday"}
print(Days1.union(Days2)) #printing th
e union of the sets
Intersection of two sets
• Intersection of two sets
• The intersection of two sets can be performed by
the and & operator or the intersection() function. The
intersection of the two sets is given as the set of the
elements that common in both sets.
Days1 = {"Monday","Tuesday", "Wednesday", "Thursday"}
Days2 = {"Monday","Tuesday","Sunday", "Friday"}
print(Days1&Days2) #prints the intersection of the t
wo sets
Intersection() method
• set1 = {"Devansh","John", "David", "Martin"}
• set2 = {"Steve", "Milan", "David", "Martin"}
• print(set1.intersection(set2)) #prints the inters
ection of the two sets
The intersection_update() method
• The intersection_update() method removes the
items from the original set that are not present
in both the sets (all the sets if more than one are
specified).
a = {"Devansh", "bob", "castle"}
b = {"castle", "dude", "emyway"}
c = {"fuson", "gaurav", "castle"}
a.intersection_update(b, c)
print(a)
Output:
{‘castle'}
Python Built-in set methods
SN
Method
Description
1
add(item)
It
adds an item to the set. It has no effect if the item is already present in the set.
2
clear()
It
deletes all the items from the set.
3
copy()
It
returns a shallow copy of the set.
4
difference_update(....)
It
modifies this set by removing all the items that are also present in the specified sets.
5
discard(item)
It
removes the specified item from the set.
6
intersection()
It
returns a new set that contains only the common elements of both the sets. (all the sets if
more
than
two are specified).
7
intersection_update(....)
It
removes the items from the original set that are not present in both the sets (all the sets if
more
than
one are specified).
8
Isdisjoint(....)
Return
True if two sets have a null intersection.
9
Issubset(....)
Report
whether another set contains this set.
10
Issuperset(....)
Report
whether this set contains another set.
11
pop()
Remove
and return an arbitrary set element that is the last element of the set. Raises KeyError
if
the
set is empty.
12
remove(item)
Remove
an element from a set; it must be a member. If the element is not a member, raise
a
KeyError.
13
symmetric_difference(...
.)
Remove
an element from a set; it must be a member. If the element is not a member, raise a
KeyError.
14
sy
mmetric_difference_
u
pdate(....)
Update
a set with the symmetric difference of itself and another.
15
union(....)
R
eturn the union of sets as a new
set.
(i.e.
all elements that are in either set.)
16
update()
Update
a set with the union of itself and others.
